# Open vs closed source AI generation tools live in two camps: **closed source** platforms that you access through a browser or API, and **open source** ecosystems you run locally or on remote workstation you can rent per compute hour. The choice shapes your cost, setup burden, and creative freedom. ^overview **Closed source** platforms are hosted services with subscription and/or usage pricing. You get instant access, strong safety filters, and predictable performance/results, but you can’t modify the underlying models or with restrictions. ^closed-source This is ideal for rapid production, teams, and users who value reliability over deep control as the platforms prioritize convenience: no local setup, usually better performances/speeds, and managed safety filters. You trade away model access and deep customization. **Open source** ecosystems give you model files and flexible tools. You gain control over prompts, workflows, and fine‑tuning, but the tradeoff is real: hardware overhead is non-trivial—[[hardware_recommendations|VRAM is the key constraint]]—and setup takes time, especially for high‑resolution or video tasks. ^open-source If you want to understand *why* local setups are possible, read [[latent_space|latent space]]. **Best fit** - ==Closed==: fast production, minimal setup, team workflows - ==Open==: customization, offline use, research, fine‑tuning